For many enterprises, big data is hard and slow. Procurement and deployment of data infrastructure can be both expensive and difficult to scale at the pace that data volumes can grow. A startup founded by former Netezza executives says that the answer to these data engineering woes is the cloud.
The startup, Cazena, came out of stealth today after two years of development with an enterprise big data-as-a-service offering intended to simplify and automate securely moving and optimizing big data processing in the cloud. It's a managed service platform that founder and CEO Prat Moghe -- who served as senior vice president of strategy, products and marketing at Netezza -- says addresses the security and complexity challenges that have kept many enterprises from migrating their big data workloads to the cloud.
The offering is now in beta with leading enterprises across several verticals.
Serving up data
The company, which just raised $20 million in Series B funding led by Formation 8, with participation from previous investors Andreessen Horowitz and North Bridge Venture Partners, is offering three services at launch -- data lake-as-a-service, data mart-as-a-service and sandbox-as-a-service -- each of which can be deployed in three clicks, Moghe says.
"Cloud is the next frontier for big data processing, yet complexity and security concerns hold most large enterprises back," Moghe says. "Cazena's big data-as-a-service is the first managed cloud service for big data processing that securely and seamlessly extends the enterprise data center with trusted cloud infrastructure and multiple, best-of-breed big data technologies. This assures the best prices and performance for any big data workload, so enterprises can focus their efforts on delivering value, not managing technology."
Moghe notes that a majority of enterprises want to leverage public cloud resources for big data analytics, but see security, data movement and complexity as hurdles. He believes Cazena's managed service platform can help enterprises overcome those hurdles. The managed service platform includes:
- Workload Intelligence. It provisions, optimizes and continually manages cloud infrastructure and best-of-breed technologies (Hadoop, MPP SQL, Spark, etc.) to guarantee workload SLAs.
- End-to-end automation. It moves data efficiently and runs analytic workloads in the cloud, with connectors for enterprise tools and data sources.
- Security and Privacy. It integrates an encrypted data cloud into the enterprise using strong security, governance and compliance controls.
- Lakes, marts and sandboxes
The data lake-as-a-service allows you to stage and query raw data like log files or streaming data, or to cost-efficiently archive historical data. The data mart-as-a-service is for augmenting existing data warehouses by offloading users or workloads to the cloud "at 1/5th the cost of traditional systems." The sandbox-as-a-service supports self-service data science to explore new ideas and hypotheses.
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